Early detection of disease and malignant tissue can lead to a better prognosis. The development of non-invasive methods for detection and characterization of tumors has an extreme importance in current medicine. Since breast cancer is one of the most common causes of cancer-related death in women and prostate cancer in men, it would be important to develop better possibilities for early detection and lesion characterization. The appropriate and accurate tumor localization/characterization and staging are very important for best choice of treatment. The most common diagnostic/screening methods for breast or prostate cancer are mammogram for breast, ultrasound and conventional magnetic resonance imaging (MRI). These methods are not always able to localize and/or characterize the cancerous and healthy tissues.
The progression and metastasis of cancer depend on the capacity of the tumor cells to interact with their microenvironment and induce angiogenesis. This induction is mediated by a large number of angiogenic factors, which collectively lead to capillary bed proliferation, increased vascularity and sprout extension into the tumor, as well as migration of the tumor cells toward the vessels. Dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) and CT provide an effective means of monitoring non-invasively and with high spatial resolution the microvascular properties of tumors. The increased permeability of tumor vasculature gives rise to increased leakage of tracers including MRI/CT contrast agents, and enables characterization of enhancement patterns in the tissue. The enhancement patterns can be analyzed by mathematical models that relate the dynamic changes in the signal intensity to physiologic parameters such as the influx and efflux transcapillary transfer constants, which are also related to the surface area and permeability of the microvasculature.
MRI and CT differentiate between solid and vascular structures, even without contrast material. MRI uses relatively harmless radio waves and there is no exposure to ionizing radiation. Due to longer acquisition time, patient movement is more detrimental.
A comparatively new method for characterization of tumor microvasculature is the dynamic contrast enhanced (DCE) MRI. For DCE-MRI or CT the multi-slice images are acquired before and during the contrast agent infusion.
In usual practice the signal intensity versus time curves are evaluated and classified according to their shape (mostly subjectively by eye). There were also developed a few other methods that create parametric maps based on signal intensities of selective time instances, or simplistic curve shape estimations to assist radiologist in case evaluations.
But there are also some complex pharmacokinetic models that were originally developed to describe the blood-brain barrier permeability, later they were modified to apply to breast lesions, and other body parts/organs. Pharmacokinetic models use the signal intensity versus time curves for quantitative assessment of permeability and microvasculature of tissues. These characteristics vary from normal to malignant tissues, thus making the ability to estimate them very valuable for comprehensive analysis and more accurate evaluation of MRI/CT exams. And being non-invasive, DCE-MRI/CT is very helpful not only for initial and early diagnosis, but also as a painless follow-up for treatments.
During pharmacokinetic analysis of MRI data, all applicable parameters and settings of MRI exam are used to fit the signal intensity curves to appropriate complex mathematical models for estimation of tissue parameters. Some examples of those models may include, but are not limited to, Brix' model, Larsson's model, Tofts' model and their modifications. For example, the Tofts' model calculates vascular permeability (k) and extracellular volume fraction (v1) of tumors. There are some specific sequences, commonly used in DCE-MRI: Gradient-Echo, Spin-Echo, Fast Low Angle Shot (FLASH). For example, for Gradient Echo sequence the ideal time curve (Enhancement curve) of each voxel is described by the following equation:
                                                                        E                ⁡                                  (                  t                  )                                            =                                                                    I                    ⁡                                          (                      t                      )                                                        -                                      I                    ⁡                                          (                      0                      )                                                                                        I                  ⁡                                      (                    0                    )                                                                                                                                          =                                                                            ⅇ                                                                        -                                                      TER                            2                                                                          ⁢                                                  C                          t                                                                                      ⁢                                                                                            [                                                      1                            -                                                                                          ⅇ                                                                                                      -                                    TR                                                                    /                                                                      T                                    10                                                                                                                              ⁢                              cos                              ⁢                                                                                                                          ⁢                              θ                                                                                ]                                                ⁢                                                  (                                                      1                            -                                                          ⅇ                                                              -                                                                  TR                                  ⁡                                                                      (                                                                                                                  T                                        10                                                                                  -                                          1                                                                                                                    +                                                                                                                        R                                          1                                                                                ⁢                                                                                                                              C                                            t                                                                                    ⁡                                                                                      (                                            t                                            )                                                                                                                                                                                                )                                                                                                                                                                                )                                                                                                                      [                                                      1                            -                                                          ⅇ                                                                                                -                                  TR                                                                /                                                                  T                                  10                                                                                                                                              ]                                                ⁢                                                  (                                                      1                            -                                                                                          ⅇ                                                                  -                                                                      TR                                    ⁡                                                                          (                                                                                                                        T                                          10                                                                                      -                                            1                                                                                                                          +                                                                                                                              R                                            1                                                                                    ⁢                                                                                                                                    C                                              t                                                                                        ⁡                                                                                          (                                              t                                              )                                                                                                                                                                                                          )                                                                                                                                                                  ⁢                              cos                              ⁢                                                                                                                          ⁢                              θ                                                                                )                                                                                                      -                  1                                            ,                                                          (        1        )            where I(t) is the signal intensity of the voxel at time t after contrast agent injection, E(t) is the enhancement and Ct(t) is the contrast agent (CA) concentration, defined as follows for Tofts' model:
                                          C            t                    ⁡                      (            t            )                          =                  Dk          ⁢                                    ∑                              i                =                1                            2                        ⁢                                                            a                  i                                ⁡                                  (                                                            ⅇ                                                                        -                          k                                                /                                                  v                          1                                                                                      -                                          ⅇ                                                                        -                                                      m                            i                                                                          ⁢                        t                                                                              )                                            /                                                (                                                            m                      i                                        -                                          k                      /                                              v                        1                                                                              )                                .                                                                        (        2        )            
All the parameters in these equations except k and v1 are defined by scan sequence parameters and other predefined or measured blood parameters: TE is the echo time; TR is the repetition time; R1 and R2 are relaxivities; T10 is the inherent tissue relaxation rate, that can be measured using the same sequence (as for DCE-MRI) before the contrast agent injection with different TR or flip angle θ; a1,2 and m1,2 are the blood plasma parameters that can be measured using the dynamic curve of a major vein or artery. Thus there are only two unknown tissue parameters permeability (k) and extracellular volume fraction (v1) that will be changed trying to find the best fit of the voxels CA concentration curve to the ideal formula above Eq. (2). For example, the Levenberg-Marquardt algorithm may be used for fitting. The quality of fit is controlled by the R2 parameter that compares the actual curve with the fitted one. If they are exactly the same R2=1, otherwise the smaller the R2, the worse is the fit. By default the fit results are accepted only if R2≧0.75 or 0.70 for higher than 50 sec/phase temporal resolution. But the user can also specify the desirable R2, if necessary. For example, each voxel is tried to fit 5 times with random initial guesses for k and v1.
For dynamic contrast enhanced CT the ideal time curve (Enhancement curve) of each voxel is described by the following equation:
                              E          ⁡                      (            t            )                          =                                                            I                ⁡                                  (                  t                  )                                            -                              I                ⁡                                  (                  0                  )                                                                    I              ⁡                              (                0                )                                              =                                    q              p                        ⁢                                                            C                  t                                ⁡                                  (                  t                  )                                            .                                                          (        3        )            Where Ct(t) is the contrast agent (CA) concentration, defined above Eq.(2) for Tofts' model. The pharmacokinetic analysis of Ct(t) is similar to the one for MRI, described above.
One of the main requirements of MRI and CT is “over-sampling”, i.e. the scanned volume is usually big enough not only to enclose the organ/region of interest but also surrounding tissues. Therefore the scanned volume is usually much bigger than the region or tissue of interest (ROI). The full pharmacokinetic analysis is applied to the all existing voxels of the dynamic, contrast-enhanced series (e.g., many million voxels for a breast MRI). Thus, the full pharmacokinetic analysis may take too much time to be performed in clinical routine on every patient. In current fast workflow of medical imaging, practitioners such as radiologists are expected to get all the information processed and proceed to interpretation/diagnosis quickly. In such environments, it is impractical for a user to manually select a region or tissue of interest, and limit a full pharmacokinetic analysis to such area. However, currently there are no algorithms that would automatically select only those voxels that are of interest for a full pharmacokinetic analysis. For such reasons, all the important information and/or results that the full pharmacokinetic analysis can provide was available only to researchers, who would manually select the appropriate small region of interest (either a small region in the image or often just 1 particular slice), and who had no time restraints on how long such full pharmacokinetic analysis may take.
Thus applying the model to all voxels in the study incurs long processing times and delays. On the other hand, manually placing ROI and thresholds are subjective and cause important spots from first glance drawn ROI to be missed. In addition, manually choosing thresholds are often prone to errors resulting in incorrect thresholds. If a chosen threshold is too high, one may miss the important parts leading to re-processing. If a chosen threshold is too low, the procedure may take a longer time. Such manual processing further leads to a need for extra examination of the case and transfers of huge amounts of image data from one station to another, slowing the entire workflow scheme of the facility.